Tool · 510(k)

510(k) Predicate Finder

Find potential predicate devices for your 510(k) submission. Enter a K-number to find all clearances with the same product code, a three-letter product code to browse clearances directly, or search by device name.

Product Code: BSN FDA class 2

Filter, Conduction, Anesthetic

Anesthesiology

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The Filter, Conduction, Anesthetic is a device used within anesthesia breathing circuits to filter particulates, bacteria, or viruses from inspired and expired gases, reducing cross-contamination risks. It is classified as FDA Class 2, requiring 510(k) premarket notification. The product code is BSN, regulated under 21 CFR 868.5130 in the Anesthesiology specialty. This device is eligible for third-party 510(k) review.

510(k) Clearances

9 matches
K Number
Device Name
Epidural MiniFilter LUER 80369-7, Epidural MiniFilter NRFit 80369-6
VENTED MILLEX -GV, CATHIVEX -GV
PORTEX EPIDURAL FILTER, MODEL 100/386/010, VARIOUS STANDARD AND CUSTOM TRAYS
MILLEX VV, GV, AND HV SYRINGE FILTER UNITS
MILLEX GP STERILIZING FILTER UNIT, MODEL SLGP 033 R
MILLEX - MP FILTER UNIT (SLMPO25SS/SLMPL25SS)
SYRINGEFILTER
ANESTHESIA CONDUCTION FILTER
FILTER TUBE

What is a Predicate Device?

A predicate device is a legally marketed device that a new 510(k) applicant uses to demonstrate substantial equivalence. The FDA's 510(k) pathway requires manufacturers to show that their device has the same intended use and similar technological characteristics as one or more predicate devices already cleared for commercial distribution.

Finding the right predicate is a critical step in 510(k) strategy. Manufacturers typically search for cleared devices with the same FDA product code, which groups devices by their intended use and regulatory classification. This tool helps you identify potential predicates by browsing all clearances within a product code.

Note that predicate devices must be legally marketed — they cannot be devices that were cleared but never commercialized, or devices subject to a mandatory recall. The determination of substantial equivalence considers intended use, technological characteristics, and performance data.

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